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Table of Contents
Preface; Acknowledgements; Contents; Acronyms; Part I Overview of Spatial Big Data Science; 1 Spatial Big Data; 1.1 What Is Spatial Big Data?; 1.2 Societal Applications; 1.3 Challenges; 1.3.1 Implicit Spatial Relationships; 1.3.2 Spatial Autocorrelation; 1.3.3 Spatial Anisotropy; 1.3.4 Spatial Heterogeneity; 1.3.5 Multiple Scales and Resolutions; 1.4 Organization of the Book; References; 2 Spatial and Spatiotemporal Big Data Science; 2.1 Input: Spatial and Spatiotemporal Data; 2.1.1 Types of Spatial and Spatiotemporal Data; 2.1.2 Data Attributes and Relationships; 2.2 Statistical Foundations
2.2.1 Spatial Statistics for Different Types of Spatial Data2.2.2 Spatiotemporal Statistics; 2.3 Output Pattern Families; 2.3.1 Spatial and Spatiotemporal Outlier Detection; 2.3.2 Spatial and Spatiotemporal Associations, Tele-Connections; 2.3.3 Spatial and Spatiotemporal Prediction; 2.3.4 Spatial and Spatiotemporal Partitioning (Clustering) and Summarization; 2.3.5 Spatial and Spatiotemporal Hotspot Detection; 2.3.6 Spatiotemporal Change; 2.4 Research Trend and Future Research Needs; 2.5 Summary; References; Part II Classification of Earth Observation Imagery Big Data
3 Overview of Earth Imagery Classification3.1 Earth Observation Imagery Big Data; 3.2 Societal Applications; 3.3 Earth Imagery Classification Algorithms; 3.4 Generating Derived Features (Indices); 3.5 Remaining Computational Challenges; References; 4 Spatial Information Gain-Based Spatial Decision Tree; 4.1 Introduction; 4.1.1 Societal Application; 4.1.2 Challenges; 4.1.3 Related Work Summary; 4.2 Problem Formulation; 4.3 Proposed Approach; 4.3.1 Basic Concepts; 4.3.2 Spatial Decision Tree Learning Algorithm; 4.3.3 An Example Execution Trace; 4.4 Evaluation; 4.4.1 Dataset and Settings
5.4.2 A Refined Algorithm5.4.3 Theoretical Analysis; 5.5 Experimental Evaluation; 5.5.1 Experiment Setup; 5.5.2 Classification Performance; 5.5.3 Computational Performance ; 5.6 Discussion; 5.7 Summary; References; 6 Spatial Ensemble Learning; 6.1 Introduction; 6.2 Problem Statement; 6.2.1 Basic Concepts; 6.2.2 Problem Definition; 6.3 Proposed Approach; 6.3.1 Preprocessing: Homogeneous Patches; 6.3.2 Approximate Per Zone Class Ambiguity; 6.3.3 Group Homogeneous Patches into Zones; 6.3.4 Theoretical Analysis; 6.4 Experimental Evaluation; 6.4.1 Experiment Setup
2.2.1 Spatial Statistics for Different Types of Spatial Data2.2.2 Spatiotemporal Statistics; 2.3 Output Pattern Families; 2.3.1 Spatial and Spatiotemporal Outlier Detection; 2.3.2 Spatial and Spatiotemporal Associations, Tele-Connections; 2.3.3 Spatial and Spatiotemporal Prediction; 2.3.4 Spatial and Spatiotemporal Partitioning (Clustering) and Summarization; 2.3.5 Spatial and Spatiotemporal Hotspot Detection; 2.3.6 Spatiotemporal Change; 2.4 Research Trend and Future Research Needs; 2.5 Summary; References; Part II Classification of Earth Observation Imagery Big Data
3 Overview of Earth Imagery Classification3.1 Earth Observation Imagery Big Data; 3.2 Societal Applications; 3.3 Earth Imagery Classification Algorithms; 3.4 Generating Derived Features (Indices); 3.5 Remaining Computational Challenges; References; 4 Spatial Information Gain-Based Spatial Decision Tree; 4.1 Introduction; 4.1.1 Societal Application; 4.1.2 Challenges; 4.1.3 Related Work Summary; 4.2 Problem Formulation; 4.3 Proposed Approach; 4.3.1 Basic Concepts; 4.3.2 Spatial Decision Tree Learning Algorithm; 4.3.3 An Example Execution Trace; 4.4 Evaluation; 4.4.1 Dataset and Settings
5.4.2 A Refined Algorithm5.4.3 Theoretical Analysis; 5.5 Experimental Evaluation; 5.5.1 Experiment Setup; 5.5.2 Classification Performance; 5.5.3 Computational Performance ; 5.6 Discussion; 5.7 Summary; References; 6 Spatial Ensemble Learning; 6.1 Introduction; 6.2 Problem Statement; 6.2.1 Basic Concepts; 6.2.2 Problem Definition; 6.3 Proposed Approach; 6.3.1 Preprocessing: Homogeneous Patches; 6.3.2 Approximate Per Zone Class Ambiguity; 6.3.3 Group Homogeneous Patches into Zones; 6.3.4 Theoretical Analysis; 6.4 Experimental Evaluation; 6.4.1 Experiment Setup